Going Deeper

Underneath every AI chatbot is one surprisingly simple idea: predicting the next word, over and over. This guide explains how a large language model turns your prompt into an answer, and why that makes it fluent yet fallible.

A neural network is the structure that lets AI learn patterns from examples. This guide explains it through one simple picture: layers of connected "neurons" passing signals along, tuned by learning from data.

AI can state a confident falsehood with no intent to deceive. This guide explains why hallucinations happen, why they are baked into the way these tools work, and how to protect yourself from them.

Ask an AI the same thing twice and you often get two different answers. This guide explains why: a setting called temperature that controls how much randomness goes into each word it chooses, and what that means for you.

AI models are often described by size, like "8 billion parameters". This guide explains what a parameter actually is, what model size does and does not tell you, and why bigger is not always better.

RAG is a way of giving AI a reference library to check before it answers, so it works from real documents rather than memory alone. In this guide you will learn what it is, how it works at a high level, and why it makes AI answers more trustworthy.

Embeddings turn text into coordinates, so that pieces of text with similar meanings end up sitting close together. In this guide you will learn what that means, why it is so useful, and how it quietly powers search and RAG.

An AI model learns by reading enormous amounts of text and slowly adjusting millions of internal settings until it gets good at predicting what comes next. In this guide you will learn what that training looks like in plain terms, and why it explains so much about how these tools behave.

A model that has only learned to predict text is knowledgeable but not naturally helpful or safe. This guide explains how human feedback turns that raw ability into the polite, useful assistant you actually talk to.

Fine-tuning takes a general AI model and gives it further, focused training so it specialises in a particular style or subject, rather like an apprenticeship. In this guide you will learn what it is, when it helps, and how it differs from giving the AI documents to read at question time.

An AI agent is a model that can take actions and use tools to work through a multi-step task, rather than only replying in chat. In this guide you will learn what that means, how it works at a high level, and an honest view of what agents can and cannot yet do.

AI image tools do not paint the way people do. Most work by removing noise, step by step, until a picture matching your words appears. This guide explains that process, called diffusion, and why AI images sometimes look strange.

Using AI well means knowing where it can go wrong: made-up facts, hidden bias, misuse such as deepfakes, and privacy. In this guide you will learn the main things to be aware of and the safeguards built to reduce harm, in a balanced, non-alarmist way.

Rules for AI are arriving around the world. This guide explains what they are all trying to do, the two main approaches (the EU's detailed law and the UK's lighter, regulator-led stance), and what it actually means for you as someone using AI day to day.